Yeast colony morphology data from Granek et al. 2013:

data_URL <- "https://tinyurl.com/36h67mhm"
yeast <- read_csv(data_URL)
head(yeast)

• Factors represent typically represent categorical variables in R. They are a unique data type that under the hood maps character strings to integers.

• Factors can be ordered or unordered

The code below illustrates the process of creating factor vectors by hand:

# unordered factor
flavors <- factor(c("sweet","sour", "sweet", "salty"))

# ordered factor
sizes <- factor(c("small", "small", "tiny", "large", "medium"), 
                levels = c("tiny", "small", "medium", "large"),
                ordered = TRUE)

flavors

[1] sweet sour  sweet salty
Levels: salty sour sweet

sizes

[1] small  small  tiny   large  medium
Levels: tiny < small < medium < large

The cut function is useful for turning numerical data into factors. The key arguments are the breaks specifying the intervals for binning the data and the labels indicating the factor categories you want to create.

x <- 1:10
x

 [1]  1  2  3  4  5  6  7  8  9 10

factored_x <- cut(x, 
                  breaks = c(0, 4, 7, 10), 
                  labels = c("small","medium","large"),
                  ordered_result = TRUE)
factored_x

 [1] small  small  small  small  medium medium medium large  large  large 
Levels: small < medium < large

Pivoting is the act of reshaping our data to make it “longer” or “wider”

• longer = fewer columns, more rows

  • Pivoting to make our data longer is frequently done when there is information in column names that we want to treat as variable values for the purposes of grouping, plotting, faceting, etc.

• wider = more columns, fewer rows

  • Pivoting to make our data wider is usually done when it’s useful to take entries in one or more columns and turn those into column headers (variable names) and take corresponding entries from other columns and make those the values in the new column

• tidyr::pivot_longer is the core function for long pivoting

First, let’s create a small example data frame to make it easier to see what pivoting is doing:

cm_example <-
  yeast |>
  select(Strain, CM.a:CM.c) |>
  slice_sample(n=2)

cm_example

Now we pivot longer on the replicate “CM” (colony morphology) columns. Note that the information in the headers become entries in the “Replicate” column:

long_cm <- 
  cm_example |> 
  pivot_longer(starts_with("CM."),
              names_to = c("Replicate"),
              values_to = c("Value"))

long_cm

Note that after pivoting, our new “Replicate” column actually contains two pieces of information. The “CM” prefix indicates what the phenotype was, while the letters “a”, “b”, and “c” indicate the replicate. We can improve on the prior code as follows:

long_cm <- 
  cm_example |> 
  pivot_longer(starts_with("CM."),
              names_sep = "\\.",  # split on the period in column nanmes
              names_to = c("Phenotype", "Replicate"),
              values_to = c("Value"))

long_cm

Pivot wider spreads column data into rows. For example, we can re-widen our previous example as follows:

long_cm

long_cm |>
  pivot_wider(names_from = c("Phenotype", "Replicate"),
              values_from = "Value")

Sometimes it’s useful to combined both long and wide pivoting. This is illustrated in the example below where we compare the relationship between the colony morphology scores (CM) and adhesiveness (Adhes) across replicates.

# subset data to focal columns for illustration
cm_and_adhes <-
  yeast |>
    select(Strain, CM.a:CM.c, Adhes.a:Adhes.c) 

head(cm_and_adhes)

# pivot longer
long_yeast <- 
  cm_and_adhes |>
  pivot_longer(starts_with(c("CM.","Adhes.")),
              names_sep = "\\.",  # split on the period in column names
              names_to = c("Phenotype", "Replicate"),
              values_to = c("Value")) 

head(long_yeast)

# pivot wider
long_then_wide_yeast <-
  long_yeast |>
  pivot_wider(names_from = "Phenotype", values_from = "Value")

head(long_then_wide_yeast)

As can be seen above, in this case we lengthened than widened, but we generated a different data structure than we started with. This new data frame allows us to generate figures like the following:

long_then_wide_yeast |>
  ggplot(aes(x = CM, y = Adhes, color = Replicate)) + 
  geom_point(alpha=0.5) +
  facet_grid(rows = vars(Replicate)) + 
  labs(x = "Colony Morphology Score",
       y = "Adhesivness")